Sentiment classification is the most rising research areas of sentiment analysis and text mining, especially with the massive amount of opinions available on social media. Recent results and efforts have demonstrated that there is no single strategy can mutually accomplish the best prediction performance on various datasets. There is a lack of existing researches to Arabic sentiment analysis compared to English sentiment analysis, because of the unique nature and difficulty of the Arabic language which leads to shortage in Arabic dataset used in sentiment analysis. An Arabic benchmark dataset is proposed in this paper for sentiment analysis showing the gathering methodology of the most recent tweets in different Arabic dialects. This dataset includes more than 151,000 different opinions in variant Arabic dialects which labeled into two balanced classes, namely, positive and negative. Different machine learning algorithms are applied on this dataset including the ridge regression which gives the highest accuracy of 99.90%.

Cancer is a class of diseases characterized by out-of-control cell growth. There are over 200 different types of cancer, and each is classified by the type of cell that is initially affected. This paper discusses the technical aspects of some of the ontology-based medical systems for cancer diseases. It also proposes an ontology based system for cancer diseases knowledge management. The system can be used to help patients, students and physicians to decide what cancer type the patient has, what is the stage of the cancer and how it can be treated. The system performance and accuracy are acceptable, with a cancer diseases classification accuracy of 92%.